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Kathrin Grosse
Person information
- affiliation: EPFL, Lausanne, Switzerland
- affiliation (former): University of Cagliari, PRALab, Italy
- affiliation (former): CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
- affiliation (former, PhD 2021): Saarland University, Saarbrücken, Germany
- affiliation (former): University of Osnabrück, Institute of Cognitive Science, Germany
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2020 – today
- 2024
- [j8]Antonio Emanuele Cinà
, Kathrin Grosse, Ambra Demontis
Machine Learning Security Against Data Poisoning: Are We There Yet? Computer 57(3): 26-34 (2024) - [j7]Hamid Eghbal-zadeh
Rethinking data augmentation for adversarial robustness. Inf. Sci. 654: 119838 (2024) - [c11]Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Battista Biggio, Alexandre Alahi:
When Your AI Becomes a Target: AI Security Incidents and Best Practices. AAAI 2024: 23041-23046 - [c10]Fiona Koh, Kathrin Grosse, Giovanni Apruzzese:
Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. HICSS 2024: 1870-1879 - [c9]Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Alexandre Alahi:
Towards More Practical Threat Models in Artificial Intelligence Security. USENIX Security Symposium 2024 - [i19]David Fernández Llorca, Ronan Hamon, Henrik Junklewitz, Kathrin Grosse, Lars Kunze, Patrick Seiniger, Robert Swaim, Nick Reed, Alexandre Alahi, Emilia Gómez, Ignacio Sánchez, Ákos Kriston:
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness. CoRR abs/2403.14641 (2024) - 2023
- [j6]Antonio Emanuele Cinà
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. ACM Comput. Surv. 55(13s): 294:1-294:39 (2023) - [j5]Michael Thomas Smith
Adversarial vulnerability bounds for Gaussian process classification. Mach. Learn. 112(3): 971-1009 (2023) - [j4]Kathrin Grosse
Machine Learning Security in Industry: A Quantitative Survey. IEEE Trans. Inf. Forensics Secur. 18: 1749-1762 (2023) - [i18]Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold, Alexandre Alahi:
Towards more Practical Threat Models in Artificial Intelligence Security. CoRR abs/2311.09994 (2023) - [i17]Kaouther Messaoud
Manipulating Trajectory Prediction with Backdoors. CoRR abs/2312.13863 (2023) - 2022
- [j3]Kathrin Grosse
Backdoor smoothing: Demystifying backdoor attacks on deep neural networks. Comput. Secur. 120: 102814 (2022) - [c8]Lukas Bieringer, Kathrin Grosse, Michael Backes, Battista Biggio, Katharina Krombholz:
Industrial practitioners' mental models of adversarial machine learning. SOUPS @ USENIX Security Symposium 2022: 97-116 - [i16]Antonio Emanuele Cinà
Machine Learning Security against Data Poisoning: Are We There Yet? CoRR abs/2204.05986 (2022) - [i15]Antonio Emanuele Cinà
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. CoRR abs/2205.01992 (2022) - [i14]Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold, Battista Biggio, Katharina Krombholz:
"Why do so?" - A Practical Perspective on Machine Learning Security. CoRR abs/2207.05164 (2022) - [i13]Ambra Demontis, Maura Pintor, Luca Demetrio
A Survey on Reinforcement Learning Security with Application to Autonomous Driving. CoRR abs/2212.06123 (2022) - 2021
- [c7]Lucjan Hanzlik, Yang Zhang
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service. CVPR Workshops 2021: 3300-3309 - [c6]Kathrin Grosse, Michael Backes:
Do winning tickets exist before DNN training? SDM 2021: 549-557 - [i12]Lukas Bieringer, Kathrin Grosse, Michael Backes, Katharina Krombholz:
Mental Models of Adversarial Machine Learning. CoRR abs/2105.03726 (2021) - [i11]Antonio Emanuele Cinà, Kathrin Grosse, Sebastiano Vascon, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo:
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions. CoRR abs/2106.07214 (2021) - 2020
- [b1]Kathrin Grosse:
Why is Machine Learning Security so hard? Saarland University, Saarbrücken, Germany, 2020 - [c5]Kathrin Grosse, Thomas Alexander Trost, Marius Mosbach, Michael Backes, Dietrich Klakow:
On the Security Relevance of Initial Weights in Deep Neural Networks. ICANN (1) 2020: 3-14 - [c4]Kathrin Grosse, Michael T. Smith, Michael Backes:
Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks. ICPR 2020: 4696-4703 - [i10]Kathrin Grosse, Taesung Lee, Youngja Park, Michael Backes, Ian M. Molloy:
A new measure for overfitting and its implications for backdooring of deep learning. CoRR abs/2006.06721 (2020) - [i9]Kathrin Grosse, Michael Backes:
How many winning tickets are there in one DNN? CoRR abs/2006.07014 (2020) - [i8]Nico Döttling, Kathrin Grosse, Michael Backes, Ian M. Molloy:
Adversarial Examples and Metrics. CoRR abs/2007.06993 (2020)
2010 – 2019
- 2019
- [i7]Kathrin Grosse, Thomas Alexander Trost, Marius Mosbach, Michael Backes, Dietrich Klakow:
Adversarial Initialization - when your network performs the way I want. CoRR abs/1902.03020 (2019) - [i6]Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A. Álvarez:
Adversarial Vulnerability Bounds for Gaussian Process Classification. CoRR abs/1909.08864 (2019) - 2018
- [i5]Kathrin Grosse, Michael T. Smith, Michael Backes:
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification. CoRR abs/1806.02032 (2018) - [i4]Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Max Augustin, Michael Backes, Mario Fritz:
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service. CoRR abs/1808.00590 (2018) - [i3]Kathrin Grosse, David Pfaff, Michael T. Smith, Michael Backes:
The Limitations of Model Uncertainty in Adversarial Settings. CoRR abs/1812.02606 (2018) - 2017
- [c3]Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel:
Adversarial Examples for Malware Detection. ESORICS (2) 2017: 62-79 - [i2]Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel:
On the (Statistical) Detection of Adversarial Examples. CoRR abs/1702.06280 (2017) - 2016
- [i1]Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel:
Adversarial Perturbations Against Deep Neural Networks for Malware Classification. CoRR abs/1606.04435 (2016) - 2015
- [j2]Kathrin Grosse, María Paula González, Carlos Iván Chesñevar, Ana Gabriela Maguitman:
Integrating argumentation and sentiment analysis for mining opinions from Twitter. AI Commun. 28(3): 387-401 (2015) - 2013
- [c2]Carlos Iván Chesñevar
A First Approach to Mining Opinions as Multisets through Argumentation. AT 2013: 195-209 - 2012
- [j1]Kathrin Grosse, Carlos Iván Chesñevar, Ana Gabriela Maguitman, Elsa Estevez:
Empowering an E-Government Platform Through Twitter-Based Arguments. Inteligencia Artif. 15(50): 46-56 (2012) - [c1]Kathrin Grosse, Carlos Iván Chesñevar, Ana Gabriela Maguitman:
An Argument-based Approach to Mining Opinions from Twitter. AT 2012: 408-422
Coauthor Index
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